Recovering Social Networks from Massive Track Datasets

Abstract

Analysis of massive track datasets is a challenging problem, especially when examining n-way relations inherent in social networks. In this paper, we use the Mitsubishi track database to examine the usefulness of three types of interaction features observable in tracklet networks. We explore ways in which social network information can be extracted and visualized using a statistical sampling of these features from a very large track dataset, with very little ground truth or outside knowledge. Special attention is given to methods that are likely to scale well beyond the size of the Mitsubishi dataset.